A \"Density-Based\" Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• A \"Density-Based\" Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models (Articolo in rivista) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1080/10618600.2013.856796 (literal)

Alternative label

• Argiento R.; Cremaschi A.; Guglielmi A. (2014)
  A "Density-Based" Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models
  in Journal of computational and graphical statistics; Taylor & Francis, Boca Raton (Stati Uniti d'America)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Argiento R.; Cremaschi A.; Guglielmi A. (literal)

Pagina inizio

• 1126 (literal)

Pagina fine

• 1142 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.tandfonline.com/doi/full/10.1080/10618600.2013.856796#abstract (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 23 (literal)

Rivista

• Journal of computational and graphical statistics (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 4 (literal)

Note

• Scopu (literal)
• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• CNR-IMATI, Milano, 20133, Italy; School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, United Kingdom; Dipartimento di Matematica, Politecnico di Milano, Milano, 20133, Italy (literal)

Titolo

• A \"Density-Based\" Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models (literal)

Abstract

• We propose a new model for cluster analysis in a Bayesian nonparametric framework. Our model combines two ingredients, species sampling mixture models of Gaussian distributions on one hand, and a deterministic clustering procedure (DBSCAN) on the other. Here, two observations from the underlying species sampling mixture model share the same cluster if the distance between the densities corresponding to their latent parameters is smaller than a threshold; this yields a random partition which is coarser than the one induced by the species sampling mixture. Since this procedure depends on the value of the threshold, we suggest a strategy to fix it. In addition, we discuss implementation and applications of the model; comparison with more standard clustering algorithms will be given as well. Supplementary materials for the article are available online. (literal)

Editore

• Taylor & Francis (Editore)

Prodotto di

• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)
• Sviluppo ed applicazione di modelli statistici (SP.P01.004.001) (Modulo)

Autore CNR

• RAFFAELE ARGIENTO (Persona)

Insieme di parole chiave

• Keywords of "A \"Density-Based\" Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)
• Sviluppo ed applicazione di modelli statistici (SP.P01.004.001) (Modulo)

Autore CNR di

• RAFFAELE ARGIENTO (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Journal of computational and graphical statistics (Rivista)

Editore di

• Taylor & Francis (Editore)

Insieme di parole chiave di

• Keywords of "A \"Density-Based\" Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models" (Insieme di parole chiave)